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arXiv 2205.04092cs.ITmath.IT
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面向工业物联网的年龄驱动联合采样与非时隙调度

Age-driven Joint Sampling and Non-slot Based Scheduling for Industrial Internet of Things

Yali Cao, Yinglei Teng, Mei Song, Nan Wang

AI总结:

本文针对工业物联网中时间敏感应用的数据新鲜度需求,联合设计采样与非时隙调度策略,通过拉格朗日方法转化马尔可夫决策过程求解单传感器优化问题,并提出多传感器低复杂度次优方案,显著降低最大时间平均信息年龄并平衡采样与服务率。

摘要:

对时间敏感的工业应用的有效控制依赖于底层传感器数据的实时传输。本文通过信息年龄(Age of Information, AoI)量化数据新鲜度,联合设计采样与非时隙调度策略,在平均能量成本和有限队列稳定性的约束下,最小化传感器间的最大时间平均信息年龄(Maximum Age of Information, MAoI)。为解决该复杂随机过程因高度耦合导致的难解性,本文首先聚焦单传感器时间平均AoI优化问题,通过拉格朗日方法将约束马尔可夫决策过程(Constrained Markov Decision Process, CMDP)转化为无约束马尔可夫决策过程(Markov Decision Process, MDP)。将无限时间平均能量与AoI表达式展开为贝尔曼方程后,可通过稳态分布概率求解单传感器时间平均AoI优化问题。此外,本文针对多传感器场景提出了一种低复杂度次优采样与半分布式调度方案。仿真结果表明,所提方案显著降低了MAoI,同时在多传感器的采样率与服务率之间实现了平衡。

英文摘要:

Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors. Quantifying the data freshness through age of information (AoI), in this paper, we jointly design sampling and non-slot based scheduling policies to minimize the maximum time-average age of information (MAoI) among sensors with the constraints of average energy cost and finite queue stability. To overcome the intractability involving high couplings of such a complex stochastic process, we first focus on the single-sensor time-average AoI optimization problem and convert the constrained Markov decision process (CMDP) into an unconstrained Markov decision process (MDP) by the Lagrangian method. With the infinite-time average energy and AoI expression expended as the Bellman equation, the single-sensor time-average AoI optimization problem can be approached through the steady-state distribution probability. Further, we propose a low-complexity sub-optimal sampling and semi-distributed scheduling scheme for the multi-sensor scenario. The simulation results show that the proposed scheme reduces the MAoI significantly while achieving a balance between the sampling rate and service rate for multiple sensors.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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